r语言 - 每 24 小时数据周期应用缩放功能

Question

我有几天的心率数据，每时每刻(随机缺失的数据间隙(，如下所示：

structure(list(TimePoint = structure(c(1523237795, 1523237796, 
1523237797, 1523237798, 1523237799, 1523237800, 1523237801, 1523237802, 
1523237803, 1523237804), class = c("POSIXct", "POSIXt"), tzone = "UTC"), 
HR = c(80L, 83L, 87L, 91L, 95L, 99L, 102L, 104L, 104L, 103L
)), row.names = c(NA, 10L), class = "data.frame")
------------------------------
TimePoint  HR
1  2018-04-09 01:36:35  80
2  2018-04-09 01:36:36  83
3  2018-04-09 01:36:37  87
4  2018-04-09 01:36:38  91
5  2018-04-09 01:36:39  95
6  2018-04-09 01:36:40  99
7  2018-04-09 01:36:41 102
8  2018-04-09 01:36:42 104
9  2018-04-09 01:36:43 104
10 2018-04-09 01:36:44 103
.
.
.

我想将 Scale(center = T， scale = T( 函数应用于数据，以便在参与者之间标准化。

• 但是，我不想在一整天的可用数据中进行规范化，而是每 24 小时对一次数据进行规范化。
• 因此，如果参与者有 3 天的数据，HR 将分别缩放到 3 次 z 分布;每次都是各自的一天

我无法成功做到这一点。

# read csv 
DF = read.csv(x)
# make sure date stamp is read YYYY Month Day & convert timestamp into class POSIXct
x2 = as.POSIXct(DF[,1], format = '%d.%m.%Y %H:%M:%S', tz = "UTC") %>% data.frame()
# rename column
colnames(x2)[1] = "TimePoint"
# add the participant HR data to this dataframe 
x2$HR = DF[,2]
# break time stamps into 60 minute windows
by60 = cut(x2$TimePoint, breaks = "60 min")
# get the average HR per 60 min window
DF_Sum = aggregate(HR ~ by60, FUN=mean, data=x2)
# add weekday /hours for future plot visualization 
DF_Sum$WeekDay = wday(DF_Sum$by60, label = T)
DF_Sum$Hour = hour(DF_Sum$by60)

我能够按时间序列拆分数据并按小时平均心率，但我似乎无法正确添加缩放功能。

感谢帮助。

Answer 1

为每个患者创建 24 小时的时间间隔，group_by患者和时间间隔，然后计算每个组的缩放心率。

library(dplyr)
df %>% 
#remove the following mutate and replace ID in group_by by the ID's column name in your data set
mutate(ID=1) %>% 
group_by(ID, Int=cut(TimePoint, breaks="24 hours")) %>%  
mutate(HR_sc=scale(HR, center = TRUE, scale = TRUE))
# A tibble: 10 x 5
# Groups:   ID, Int [1]
TimePoint              HR    ID Int                   HR_sc
<dttm>              <int> <dbl> <fct>                 <dbl>
1 2018-04-09 01:26:35    80     1 2018-04-09 01:00:00 -1.63  
2 2018-04-09 01:28:16    83     1 2018-04-09 01:00:00 -1.30  
3 2018-04-09 01:29:57    87     1 2018-04-09 01:00:00 -0.860 
4 2018-04-09 01:31:38    91     1 2018-04-09 01:00:00 -0.419 
5 2018-04-09 01:33:19    95     1 2018-04-09 01:00:00  0.0221
6 2018-04-09 01:33:20    99     1 2018-04-09 01:00:00  0.463 
7 2018-04-09 01:35:01   102     1 2018-04-09 01:00:00  0.794 
8 2018-04-09 01:36:42   104     1 2018-04-09 01:00:00  1.01  
9 2018-04-09 01:38:23   104     1 2018-04-09 01:00:00  1.01  
10 2018-04-09 01:39:59   103     1 2018-04-09 01:00:00  0.905